Extracting structured data from weblogs

ABSTRACT

Methods and apparatus for extracting structured data from weblogs are disclosed. In some examples, the methods and apparatus include retrieving a feed referenced on a webpage of the weblog and, in response to determining that the feed does not contain a first portion of a weblog post, creating, via a processor, a representation of the weblog post based on a second portion of the weblog post included in the feed, searching, via the processor, the weblog for the second portion of the weblog post, when the second portion of the weblog post is found in the weblog, identifying, via the processor, a node associated with the second portion in the webpage, and modifying, via the processor, the representation based on information from within the node to reconstruct the weblog post.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 11/454,301, which was filed on Jun. 16, 2006, which claims thepriority of U.S. Provisional Patent Application Ser. No. 60/691,200,which was filed Jun. 16, 2005. U.S. patent application Ser. No.11/454,301 and U.S. Provisional Patent Application Ser. No. 60/691,200are incorporated by reference in their entirety.

BACKGROUND

Weblogging or “blogging” has emerged in the past few years as a newgrassroots publishing medium. Like electronic mail and the web itself,weblogging has taken off and by some estimates the number of weblogs isdoubling every 6 months. As of June 2006, BlogPulse estimates place thenumber of active weblogs at nearly 10 million blogs, of which about 36%have had at least one post in the past 3 months. BlogPulse findsapproximately 60,000 new weblogs each day. Statistics published by otherblog search engines such as Technorati and Pub Sub are similar. However,these estimates may well be excluding large numbers of non-Englishlanguage weblogs.

A weblog is commonly defined as a web page with a set of dated entries,in reverse chronological order, maintained by its writer via a weblogpublishing software tool. We can define each entry as a set of one ormore time-stamped posts; an author may typically post several times aday. This is a matter a style, as some authors post at most once a dayin an all-inclusive entry. Others prefer to micro-post, making eachpublished item a separate post in the day's entry.

Due to the popularity of weblogs, there is a need for a method ofsearching individual posts within weblogs. The present inventionaddresses this need.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a sample page from a weblog.

DETAILED DESCRIPTION 1. Overview

The present invention provides a process for segmenting weblogs intoposts. Weblogs can facilitate communication and dissemination of contentin any environment having two or more workstations in mutualcommunication. While weblogs are typically hosted by a server connectedto the Internet, the concept can include other types of networks, suchas local area networks (LANs), wide area networks (WANs), and publicdata networks, by which client workstations obtain data from a serverworkstation.

Each workstation may comprise a microcomputer such as a personalcomputer, for example, including a system bus that is connected to acentral processing unit (CPU) and to memory, including read only memory(ROM) and random access memory (RAM). The system bus can be connected,via appropriate interface known to persons skilled in the art, tovarious input/output devices, including additional nonvolatile datastorage devices, video and audio adapters, keyboard, mouse, and otherdevices that provide input to the workstation or receive output from theworkstation. The Workstation can also include a data port forcommunicating with other constituents of collaborative data processingenvironment. The data port may be a serial port for linking workstationto a modem or a communications adapter for connecting workstation to aLAN.

Each workstation also typically includes software programs that arestored on the data storage devices or retrieved from other parts ofcollaborative data processing system and loaded into RAM and then intoCPU for execution. Among those programs is a client program thatreceives messages from, and transmits messages to, other workstationsconnected to the network.

Web search engines such as Google, Yahoo, and MSN Search index theentire content of a web page typically every few days. However, forweblogs, users want to be able to search over individual posts, and innear real-time. Weblog search portals such as Technorati, Feedster,PubSub and BlogPulse have gained in popularity over the past year and ahalf, as people begin to turn to weblogs to get up-to-the-minutebreaking news and to get fresh angles on news stories.

In addition, marketers have awakened to the possibility of miningconsumer sentiment from weblogs. In order to produce accurate analytics,it is first necessary to be to identify individual weblog posts.Examples of consumer sentiment analytics are the buzz surrounding aproduct (number of mentions), number of links to a company website,trends in number of mentions and number of links, and ratio of positivevs. negative mentions. Glance, M. Hurst, K. Nigam, M. Siegler, R.Stockton, and T. Tomokiyo. Analyzing online discussion for marketingintelligence. In Proceedings WWW-2005, Chiba, Japan, 2005 (incorporatedherein by reference).

Researchers as well are turning to blogs to gauge opinion and communitystructure. For example, Adamic and Glance recently analyzed the linkingbehavior of political bloggers during the 2004 U.S. PresidentialElection and found that conservative bloggers link to each other morefrequently and in a denser pattern than liberal bloggers. Adamic and N.Glance, The political blogosphere and the 2004 U.S. election: Dividedthey blog, In Proceedings WWW-2005 2nd Annual Workshop on the WebloggingEcosystem, Chiba, Japan, 2005 (incorporated herein by reference). Marlowhas studied the structure and authority in weblogs using inter-postcitation counts. Marlow. Audience, structure and authority in the weblogcommunity, In International Communication Association Conference, NewOrleans, La., 2004 (incorporated herein by reference). Adar et. al. haveexplored how memes thread through the blogsphere from post to post.Adar, L. Zhang, L. A. Adamic, and R. M. Lukose, Implicit structure andthe dynamics of blogspace, In Proceedings WWW-2004 Workshop on theWeblogging Ecosystem, New York City, N.Y., 2004 (incorporated herein byreference). The Global Attention Profiles project tracks the attentionthat bloggers pay to different nations of the world, in comparison withselected mainstream media outlets.

To enable sophisticated analytics over weblogs, a blog search enginetypically uses an indexing mechanism that indexes a weblog one post attime, as opposed to one HTML page at a time. In order to index blogs onepost at a time, the indexing system should be able to segment the weblogHTML into individual posts and extract meta-data associated with theposts, such as the posting date, title, permalink, and author.

The present invention provides a method for segmenting weblogs intoindividual posts using a combination of weblog feeds (such as RSS andAtom) and model-based wrapper segmentation. RSS is a family of web feedformats, specified in XML and used for Web syndication. Web feedsprovide web content or summaries of web content together with links tothe full versions of the content, and other metadata. RSS, inparticular, delivers this information as an XML file called an RSS feed,webfeed, RSS stream, or RSS channel. In addition to facilitatingsyndication, web feeds allow a website's frequent readers to trackupdates on the site using an aggregator. Atom is the name of a specificweb feed format. Web feeds, from a user's perspective, allow Internetusers to subscribe to websites that change or add content regularly. Webfeeds in general provide web content or summaries of web contenttogether with links to the full versions of the content, and othermeta-data in a developer-friendly standardized format Atom, from atechnical perspective, is an open standard that includes an XML-basedweb syndication format used by weblogs, news websites and web mail.

2. Definitions

The following definitions are used throughout this description:

Weblog or blog: a weblog is a website where an individual or group ofindividuals publishes posts periodically. The posts are usuallydisplayed in reverse chronological order. Each post generally consistsof: a date, a title, the body of the post, a permalink to the post, anauthor, and one or more categorizations.

Weblog entry: a post or a set of posts published on a specific day.

Post: item published to weblog at a specific time of day.

Weblog feed/syndication: weblogs may or may not make posts available viasyndication using RSS or Atom feeds. Web feeds provide web content orsummaries of web content together with links to the full versions of thecontent, and other metadata. Atom feeds are XML documents. In addition,there are several versions of the RSS standard in use.

Weblog host: a company or website that hosts weblogs for individuals.Examples of popular weblog hosts are: livejournal.com, xanga.com,spaces.msn.com, blogspot.com, and the family of per-country domaintypepad hosts.

Weblog software: software that enables creation and publishing of weblogposts to a weblog host, or to a self-hosted weblog. Each weblog host hasits own weblog software tool for publishing posts. In addition, thereare a number of weblog software tools for publishing a self-hostedweblog, such as Typepad, Moveable Type, and Wordpress.

Weblog ping: A weblog ping is an XML-RPC mechanism that notifies a pingserver, such as weblogs.com or blo.gs, that the weblog has changed(e.g., the author has written a new post). Many weblog software toolscan be set (or are automatically pre-set) to ping centralized serverswhenever the weblog is updated. Example ping servers arehttp://blogs/ping.php and http://rpc.technorati.com/rpc/pingl. Some pingservers accept “extended pings” that include both the URL and feed URLof the updated weblog.

Crawl: A web crawler (also known as a web spider or web robot) is aprogram which browses the World Wide Web in a methodical, automatedmanner. A web crawler is one type of bot, or software agent. In general,it starts with a list of URLs to visit, called the seeds. As the crawlervisits these URLs, it identifies all the hyperlinks in the page and addsthem to the list of URLs to visit, called the crawl frontier. URLs fromthe frontier are recursively visited according to a set of policies.

Screen scraping: a technique in which a computer program extracts datafrom the display output of another program. The program doing thescraping is called a screen scraper. The key element that distinguishesscreen scraping from regular parsing is that the output being scrapedwas nominally intended for human consumption, not machineinterpretation. There are a number of synonyms for screen scraping,including: Data scraping, data extraction, web scraping, page scraping,and HTML scraping (the last three being specific to scraping web pages).

Wrapper: a program that performs screen scraping.

“Document Object Model” (DOM): a description of how an HTML or XMLdocument is represented in an object-oriented fashion. DOM provides anapplication programming interface to access and modify the content,structure and style of the document.

Permalink: a term used in the world of blogging to indicate a URL whichpoints to a specific blog entry.

XPath (XML Path Language): a terse (non-XML) syntax for addressingportions of an XML document.

3. Process for Extracting Posts from a Weblog

Here we describe a process for extracting individual posts from aweblog, according to an exemplary embodiment of the present invention.First we describe the typical layout of a weblog.

3.1. Modelling Weblogs

FIG. 1 shows the home page of a well-known weblog. Notice the extraneouscontent on the page: header, footer (not displayed) and sidebars (inthis example, ads). However, the main content is a sequence of entriesordered in reverse chronological order, with each entry consisting ofsequence of posts, also in reverse chronological order.

A weblog can be described formally as follows:

-   -   Weblog: Entry+    -   Entry: Date Post+    -   Post: Title? Content Permalink? Author? Timestamp? Link to        comments? Categories*

The ordering of the sub-elements for the Entry elements and the Postelements is typically not standardized across weblogs, although it isassumed to be fixed within a weblog.

Also, the model assumes that the entry dates are monotonicallydecreasing.

3.2. Weblog Syndication

Many weblog publishing software tools also publish a feed in associationwith the weblog. The feed is updated whenever a new item is posted tothe weblog. The feed is a “pull” mechanism, as is the weblog page. As a“pull” mechanism, the feed is accessed in order to find out if theweblog has been updated. However, feeds are designed to be read via afeed reader/aggregator (such as Bloglines, NewsGator, etc. or via anextension to a mail reader), which polls the feed on the behalf of theuser(s). Thus, the end user who reads feeds via a feed readerexperiences weblogs as a “push” phenomena: the newly published weblogposts are pushed to the user's screen.

Some weblog software tools have provided customization of the weblog'sfeed: the publication of the feed can be turned on or off, the feed canbe updated whenever a new item is posted or modified, and the feed canbe full content or partial content. Full vs. partial content is animportant distinction. We define a full content feed as a feed thatpublishes the entire content of the post as viewable on the front pageof the weblog. We define a partial content feed as a feed that publishesa summary of the post content available via the weblog.

With respect to feed publication, weblog software tools fall into threecategories: (1) automatic generation of feeds (partial or full); (2)customized generation of feeds; or (3) no feed generation capability. Inthe last case, some tech-savvy bloggers will use custom software tocreate a feed and associate it with their weblog, or turn to athird-party feed generator to host a feed for the weblog (e.g.,FeedBurner: http://www.feedburner.com/).

3.3. Segmenting Weblogs into Posts

This section describes our approach for segmenting weblogs into posts,according to an exemplary embodiment of the present invention. It wouldbe costly to manually create individual wrappers for each weblog.However, weblogs tend to conform to a common model, as described inSection 3.1 above. Thus, we have focused on developing an approach thatgeneralizes well over the majority of weblogs.

If a full content feed is available for a weblog, then the task ofextracting posts from the weblog is the straightforward mapping of theXML, format to an internal format. If a partial content feed exists fora weblog, then we use the partial content to guide the extractionprocess. If no partial content feed exists for a weblog, then we apply amodel-based approach to extracting posts from the weblog page, takingadvantage of regularities more or less common to most weblogs. Our workon model-based segmentation is similar to that of Nanno et al. Nanno,Automatic collection and monitoring of Japanese weblogs, In ProceedingsWWW-2004 Workshop on the Weblogging Ecosystem, New York City, N.Y., 2004(incorporated herein by reference).

Accordingly, here is an outline of the algorithm used for extractingposts from a weblog, according to an exemplary embodiment of the presentinvention:

1. Crawl home page of weblog.

2. Discover feed(s) associated with weblog

3. For each feed:

-   -   (a) Determine if feed satisfies minimal requirements for        proceeding. Our feed finder considers an item in the feed        sufficient if it contains, at minimum, the following fields:        date-posted AND (content OR description).    -   (b) If the feed is sufficient, classify the feed as full content        or partial content.    -   (c) If feed is full content, then we map the data found in the        feed into a representation for weblog posts.    -   (d) If feed is partial content, then use feed data to guide        screen scraping of the weblog to construct a representation for        weblog posts.    -   (e) If the feed has insufficient content, then try next feed        associated with weblog.

4. If there are no feeds with sufficient full or partial content, thenfall back on screen scraping of weblog. Screen scraping uses amodel-based approach to segment the weblog page into posts using textualand HTML elements of the page as markers.

3.4. Feed Discovery

After reaching the home page of the weblog, the first step consists ofdiscovering the feed(s) for the weblog. If the weblog update wascollected from a ping server relaying extended pings, and if theaccepted ping includes the feed URL for the weblog, then we have locatedthe feed. Alternatively, if the weblog is hosted by a weblog host whichpublishes full content feeds for its weblogs, then we need only map theweblog URL to the feed URL.

Otherwise, the next step in discovering the feed(s) for a weblog is touse “RSS auto-discovery.” RSS auto-discovery is an agreed-upon standardfor specifying the location(s) of a weblog feed(s) as metadata in theHTML for the weblog home page.

If RSS auto-discovery fails to find a set of feeds for the weblog, thenext step is to search for links to feeds from body of the weblog.First, all hyperlinks are extracted from the weblog. Next, the set ofextracted hyperlinks are filtered using a classifier to identify whichone(s) belong to the set of feeds for the weblogs. Currently, we use aset of heuristics to identify the feed(s) for a weblog from theextracted hyperlinks. The following is a non-exclusive list of criteriathat can be used to identify the feed:

-   -   URLs that allow readers to subscribe to the feed in their RSS        reader; these urls match “?url=?” or “bloglines.com/sub/”    -   URLs with one of a set of common feed suffixes, including        {“atom.xml”, “.xml”, “.rss”, “rdf”, . . . } AND matching the        host name of the blog    -   URLs with a host with one of a set of common feed prefixes,        including {“rxml”, “rss”, . . . } AND matching the domain name        of the blog.

3.5. Full Content vs. Partial Content Feeds

The multiple XML standards for weblog feeds (several versions of RSS andAtom) all satisfy the following minimal conditions:

-   -   The feed has the following top-level fields: weblog url, weblog        title    -   The feed consists of a set of items (which for weblogs,        correspond to posts). Each item may have the following fields:        date-posted, permalink, post title, author, content, description

Our feed finder considers an item in the feed content to be sufficientif it contains, at minimum, the following fields: date-posted AND(content OR description). If no items in the feed contain sufficientcontent, the feed is rejected and weblog segmentation falls back uponmodel-based weblog segmentation (aka screen scraping).

The actual names of the fields depend on the feed standard being used.For example, for RSS v0.91, date-posted maps onto the XPath/item/title;content maps onto the XPath/item/description; and description maps ontothe XPath/item/description. (There is no separate content field in theRSS v0.91 specification.)

Typically, the description field is used to provide a summary of thepost (usually the first few lines) while the content field is used toprovide either the full content of the post or a summary. Some feedscontain both, in which case, typically, the description contains thesummary and the content contains the full post.

The feed classifier, which classifies the feed as full content orpartial content, takes as input features of the content and descriptiontext, such as: presence/absence of HTML tags, % posts ending inellipses, and type of feed. Based on these features, it uses heuristicsdecides whether or not the items in the feed are full content. Otherfeatures could be added, such as the variance in the length of text,etc.

If the feed is classified as full content, then we map the data found inthe feed into our own internal representation for weblog posts, usingXML representation of the content of the post+meta-data. Elements in theXML representation include: weblog url, permalink, weblog title, posttitle, date posted, time posted, and content.

If the feed is not full content, then we create skeletal posts from thedata in the feed. For each post, we fill in the following data: weblogurl; date-posted; partial content; post title (if found); post author(if found); and permalink (if found).

3.6. Feed-guided Weblog Segmentation

The next step is to fill in the skeletal posts constructed from the feedby using the content of the weblog page itself. Missing from theskeletal posts is the full content of the post. To find the fullcontent, the partial content is first processed to remove summarizationartifacts (e.g., ending ellipsis). Then, we search for the partialcontent in the weblog. If the partial content is not found, then we willomit that particular post from our segmentation because not enoughinformation can be located to construct the post. If we end up findinginsufficient information on all posts, then we will fall back onmodel-based segmentation.

If the partial content matches text on the weblog home page, then wefind the enclosing node for the matching text in the tidied XHTML forthe weblog page. The Extensible HyperText Markup Language, or XHTML, isa markup language that has the same expressive possibilities as HTML,but a stricter syntax. The text inside the enclosing node is then usedas the content for the post. If enclosing nodes for successive postsoverlap, then we throw an error indicating that feed-guided segmentationhas failed for the weblog, and, again, fall back on model-basedsegmentation.

3.7. Model-based Weblog Segmentation

If there are no feeds with sufficient full or partial content, then weattempt to segment the weblog into posts using screen scraping of theweblog. Screen scraping uses a model-based approach to segment theweblog page into posts using textual and HTML elements of the page asmarkers.

Model-based weblog segmentation assumes that weblogs can be modeled asdescribed in Section 3.1. Our approach then starts from a simplificationof that model: (date ([title] content)+)+. This model assumes that datesappear first. This means that if we are able to extract the weblog entrydates, then we can use the dates as markers for the entries. Of course,a weblog page may have many other dates apart from the dates marking theentries: dates in the content of the posts; dates in the sidebars or inother non-weblog content included in the HTML page. However, as weblogsare produced by weblog software, we can expect certain regularities inthe underlying DOM of the generated HTML. In particular, we expect thatthe relative XPaths of the weblog entry dates to be identical. Arelative XPath is an Xpath that is defined relative to a location (XMLnode) in an XML document. In practice we've found that the relativeXPaths of the entry dates are identical if we ignore certain elements inthe XPath:/align/and repeating/font/s.

The first step in our model-based segmentation algorithm consists ofextracting all the dates from the tidied XHTML for the weblog page usinga date extractor. The dates are sorted into ordered lists, one list foreach unique relative XPath. The order within the list corresponds to theordering of the dates with the DOM for the weblog page.

We then filter the lists according to a set of heuristics in order toidentify which list corresponds to the actual weblog entry dates. Thefiltering process for the date lists can be performed using thefollowing sequence of steps:

-   -   1. Keep only lists whose dates all belong to the current year        and/or the past year.    -   2. Keep only non-singleton date lists.    -   3. Keep only lists whose dates conform to a similar format (e.g.        MM/DD/YYYY).    -   4. Keep only lists whose dates decrease monotonically.    -   5. Keep only lists with most recent dates (but not in the        future).    -   6. Keep only lists with longest date string representation.    -   7. Keep only lists with the greatest number of dates.    -   8. Keep only first list.

One might think that after step 5 in the filtering process, we would beleft with at most one list of dates. In practice, this is frequently notthe case, because weblogs often have a sidebar with a dated list ofrecent posts which corresponds exactly the full set of posts in the mainpart of the weblog. The last few filtering steps help correctly identifythe weblog entry dates as opposed to the dates in the sidebar.

If we fail to find a conforming list of dates, then model-basedsegmentation fails. There are some known cases where our approach fails:when only one entry appears on the home page of the weblog; or whenweblog software for some reason generates irregular XPaths for the datesand/or content. But in many cases, segmentation fails when the HTML pagein question is not actually a weblog. Thus, our model-based segmentationalgorithm has the additional functionality of serving as a classifierthat identifies whether or not an HTML page is indeed a weblog.

Once we have identified the entry dates for the weblog, model-basedsegmentation proceeds as follows:

1. Segment weblog into entries, using dates as markers.

2. Segment each weblog entry into posts using post titles markers.

3. For each post, identify permalink and author.

In step 1, we assume that all DOM nodes between subsequent entry datesform the weblog entry associated with the earlier date. The maindifficulty is identifying the end of the last post. For this we use aset of heuristics to identify the end of the blog entry by looking forthe start of boilerplate weblog end template. Example end markersinclude: the start of a sidebar, a copyright notice, or a form, or acomment. Another heuristic for finding the end of the blog entry is tolook for a node in the DOM whose XPath is analogous in structure to theXPath of the last node in the previous weblog entry.

In step 2, we attempt to use post titles to demarcate boundaries betweenposts for an entry. First, we iterate over the nodes of the entrysearching for a node that matches one of our conditions for being atitle node. These conditions include: class attribute of the node equals‘title’ or ‘subtitle’ or ‘blogpost’, etc. Once we have found the firstmatching title, we then assume that all subsequent post titles will havethe same relative XPath. Again, we assume that all DOM nodes betweensubsequent title nodes are associated with the earlier title.

If we are unable to find titles, then we treat the entire entry as asingle post. In fact, we have found that the majority of bloggers do notpost more than once per day.

The final post-processing step identifies the permalink and author fromthe content of each extracted post using common patterns for permalinksand author signatures. To find authors, we look for patterns like“posted by.” To find permalinks, we look for hrefs (hyperlinks) in thepost content that match, for example, “comment” or “archive.” Somepatterns are given higher priority than others for matching againstpermalinks.

A weakness of our current implementation of model-based wrappersegmentation is that it assumes that the date field comes first in aweblog entry. In fact, while most blogs exhibit the pattern date([title] content)+, others use (title date content)+ or even ([title]content date)+. Our approach is still able to segment blogs exhibitingthese less common patterns, although the segmentation associates thedate with the incorrect content. That is, if we have a sequence of Nposts (post 1 through post N), the date for post 1 will be associatedwith the content of post 2 and so on. In addition, we will fail toextract the content of post 1. We call this error a parity error.

4. Segmentation Statistics

We have implemented weblog segmentation as part of the BlogPulse weblogpost collection, indexing and search system.

In tests of the model-based segmentation algorithm, we have found thatthe precision of this algorithm is about 90%—that is about 90% ofextracted posts have date, title and content fields that correspond tothose of actual posts on the weblogs. The recall is approximately70%—that is, we are able to extract posts from about 70% of trueweblogs.

TABLE 1 Segmentation statistics for Apr. 13, 2005 Segmentation method %of weblogs Full content feed 78% Feed-guided segmentation 11%Model-based segmentation 11%

Table 1 shows the statistics for our segmentation process, thepercentage of weblogs segmented using: (1) full content feeds (78%); (2)feed-guided segmentation (11%); or (3) model-based segmentation (11%).

We have implemented our segmentation algorithm as part of the weblogpost collection subsystem of BlogPulse. This enables BlogPulse toprovide search over individual blog posts. Furthermore, the corpus ofdated weblog posts serves as a data set for tracking trends over time,and for analyzing how memes spread through the blogosphere.

Having described the invention with reference to embodiments, it is tobe understood that the invention is defined by the claims, and it is notintended that any limitations or elements describing the embodiments setforth herein are to be incorporated into the meanings of the claimsunless such limitations or elements are explicitly listed in the claims.Likewise, it is to be understood that it is not necessary to meet any orall of the identified advantages or objects of the invention disclosedherein in order to fall within the scope of any claims, since theinvention is defined by the claims and since inherent and/or unforeseenadvantages of the present invention may exist even though they may nothave been explicitly discussed herein.

What is claimed is:
 1. A method of extracting weblog posts from aweblog, the method comprising: retrieving a feed referenced on a webpageof the weblog; and in response to determining that the feed does notcontain a first portion of a weblog post: creating, via a processor, arepresentation of the weblog post based on a second portion of theweblog post included in the feed; filtering the representation of theweblog post to summarization artefacts; searching, via the processor,the weblog for the filtered representation of the second portion of theweblog post; when the second portion of the weblog post is found in theweblog, identifying, via the processor, a node associated with thesecond portion in the webpage; extracting, via the processor,information from markup language contained within the node associatedwith the second portion of the webpage; and modifying, via theprocessor, the representation based on the information extracted fromwithin the node to reconstruct the weblog post.
 2. A method as definedin claim 1, further including, in response to determining that the feedcontains the first portion and the second portion, mapping the first andsecond portions into the representation of the weblog post.
 3. A methodas defined in claim 1, wherein the first portion is at least one of adate for the weblog post, a permalink of the weblog post, a post titleof the weblog post, an author of the weblog post, or a summary of theweblog post.
 4. A method as defined in claim 1, wherein the determiningthat the feed does not contain the first portion of the weblog post isbased on at least one of a presence of tags, a percentage of postsincluding ellipses, or a variance in length of the weblog post.
 5. Amethod as defined in claim 1, further including, in response todetermining that the feed does not contain a date of the weblog post andat least one of a summary or a full description of the weblog post:extracting dates from the markup language of the webpage; sorting theextracted dates into ordered lists; filtering the ordered lists todetermine which of the lists correspond to entry dates of the weblogpost; segmenting the weblog into entries based on dates from thefiltered list as markers for the entries; extracting the weblog postfrom the weblog entries based on post title markers; and identifying apermalink and an author of the weblog post.
 6. A method as defined inclaim 5, wherein the filtering of the ordered lists includes: extractinglists whose dates belong to a current year or a past year; extractingnon-singleton date lists; extracting lists whose dates conform to asimilar format; extracting lists whose dates decrease monotonically;extracting lists with most recent dates; extracting lists with a longestdate string representation; and extracting lists with a greatest numberof dates.
 7. A method as defined in claim 1, further including screenscraping the weblog.
 8. An apparatus for extracting weblog posts from aweblog, the apparatus comprising: a web crawler to retrieve a feedreferenced on a webpage of the weblog; a feed classifier to determinewhether the feed contains a first portion of a weblog post; and awrapper to, in response to determining that the feed does not contain afirst portion of a weblog post: filter the representation of the weblogpost to summarization artefacts; create a representation of the weblogpost based on a second portion of the weblog post included in the feed;search the weblog for the filtered representation of the second portionof the weblog post; when the second portion of the weblog post is foundin the weblog, identify a node associated with the second portion in thewebpage; extract information from markup language contained within thenode associated with the second portion of the webpage; and modify therepresentation based on the information extracted from within the nodeto reconstruct the weblog post, at least one of the web crawler, thefeed classifier, or the wrapper implemented by a logic circuit.
 9. Anapparatus as defined in claim 8, wherein the wrapper is to, in responseto determining that the feed contains the first portion and the secondportion, map the first and second portions into the representation ofthe weblog post.
 10. An apparatus as defined in claim 8, wherein thefirst portion is at least one of a date for the weblog post, a permalinkof the weblog post, a post title of the weblog post, an author of theweblog post, or a summary of the weblog post.
 11. An apparatus asdefined in claim 8, wherein the feed classifier is to determine whetherthe feed contains the first portion of the weblog post based on at leastone of a presence of tags, a percentage of posts including ellipses, ora variance in length of the weblog post.
 12. An apparatus as defined inclaim 8, further including a date extractor to, in response todetermining that the feed does not contain a date of the weblog post andat least one of a summary or a full description of the weblog post:extract dates from the markup language of the webpage; sort theextracted dates into ordered lists; filter the ordered lists todetermine which of the lists correspond to entry dates of the weblogpost; segment the weblog into entries based on dates from the filteredlist as markers for the entries; extract the weblog post from the weblogentries based on post title markers; and identify a permalink and anauthor of the weblog post.
 13. An apparatus as defined in claim 12,wherein to filter the ordered lists, the date extractor is to: extractlists whose dates belong to a current year or a past year; extractnon-singleton date lists; extract lists whose dates conform to a similarformat; extract lists whose dates decrease monotonically; extract listswith most recent dates; extract lists with a longest date stringrepresentation; and extract lists with a greatest number of dates. 14.An apparatus as defined in claim 8, wherein the wrapper is to screenscrape the weblog.
 15. A computer readable storage hardware device orstorage disc comprising instructions, that when executed, cause amachine to at least: retrieve a feed referenced on a webpage of theweblog; and in response to determining that the feed does not contain afirst portion of a weblog post: create a representation of the weblogpost based on a second portion of the weblog post included in the feed;filter the representation of the weblog post to summarization artefactssearch the weblog for the filtered representation of the second portionof the weblog post; when the second portion of the weblog post is foundin the weblog, identify a node associated with the second portion in thewebpage; extract information from markup language contained within thenode associated with the second portion of the webpage; and modify therepresentation based on the information extracted from within the nodeto reconstruct the weblog post.
 16. A hardware device as defined inclaim 15, wherein the instructions, when executed, cause the machine to,when the feed contains the first portion and the second portion, map thefirst and second portions into the representation of the weblog post.17. A hardware device as defined in claim 15, wherein the first portionis at least one of a date of the weblog post, a permalink of the weblogpost, a post title of the weblog post, an author of the weblog post, ora summary of the weblog post.
 18. A hardware device as defined in claim15, wherein the instructions, when executed, cause the machine to,determine the feed does not contain the first portion of the weblog postbased on at least one of a presence of tags, a percentage of postsincluding ellipses, or a variance in length of the weblog post.
 19. Ahardware device as defined in claim 15, wherein the instructions, whenexecuted, cause the machine to, in response to determining that the feeddoes not contain a date of the weblog post and at least one of a summaryor a full description of the weblog post: extract dates from the markuplanguage of the webpage; sort the extracted dates into ordered lists;filter the ordered lists to determine which of the lists correspond toentry dates of the weblog post; segment the weblog into entries based ondates from the filtered list as markers for the entries; extract theweblog post from the weblog entries based on post title markers; andidentify a permalink and an author of the weblog post.
 20. A hardwaredevice as defined in claim 19, wherein to filter the ordered lists, theinstructions, when executed, cause the machine to: extract lists whosedates belong to a current year or a past year; extract non-singletondate lists; extract lists whose dates conform to a similar format;extract lists whose dates decrease monotonically; extract lists withmost recent dates; extract lists with a longest date stringrepresentation; and extract lists with a greatest number of dates.
 21. Ahardware device as defined in claim 15, wherein the instructions, whenexecuted, cause the machine to screen scrape the weblog.